根据索引列合并数据帧

问题描述

我可以看到我想通过concat做的事情:在索引

I can see that what I'm trying to do is possible via concat: Merge dataframes on index

为什么不能使用merge做等效的事情?

Why can I not do something equivalent using merge?

import pandas as pd

df = pd.DataFrame({'name':['joe strummer','johnny rotten'],'age':[73,80]})
df2 = pd.DataFrame({'name':['nancy','sid'],'age':[17,19]})

df.index.name = 'x'
df2.index.name = 'y'

pd.merge(df2,df, left_on='y',right_on='x')

推荐答案

这是可能的；只需使用left_index=True代替left_on，并使用right_index=True代替right_on:

This is possible; just use left_index=True instead of left_on, and right_index=True instead of right_on:

>>> pd.merge(df, df2, left_index=True, right_index=True)
   age_x         name_x  age_y name_y
0     73   joe strummer     17  nancy
1     80  johnny rotten     19    sid

使用left_on不起作用，因为索引是DataFrame列的单独对象.索引可以具有名称，甚至可以与您的某一列具有相同的名称，但是left_on不会看到它，因为它仅查看列名称.

Using left_on doesn't work because indexes are separate objects to DataFrame columns. An index can have name, even an identical name to one of your columns, but left_on won't see it because it only looks at column names.

合并文档给出了这些论据的指导如下:

The documentation for merge gives the following guidance for these arguments:

使用左侧DataFrame中的索引作为连接键.如果它是一个MultiIndex，则另一个DataFrame中的键数(索引或列数)必须与级别数匹配

Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels

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